Blitar district has become one of the many cities in Java the land situation is largely a good soil of vuikanik to be used as farmland. Agriculture is one of the priority sectors in Blitar district and is supported by culture, geographical conditions and the number of people whose livelihoods are farmers.Hence, it requires a way of knowing where a region might have a potential paddy commodity. It is hoped that the government of blitar will be able to make the best use of the number of paddy commodities produced in blitar district with the many farmers available. A rough set is able to produce information with a rule pattern (rule) which can determine the potential areas for paddy commodities in Blitar district by using factors of harvested area, production amount, and number of farmers per sub-district. This research is not only done analytically but also help from Rosetta's software to test analytic data analysis use rough set. The result of this study is rule as many as 38 rule that can explain the possibility of stake based on the 3 decision attributes: potential, low potential, and not potential. For those areas there is a good chance paddy commodity potential area based on the rules that have been formed is area have a large crop, a large amount of paddy produced, and a small number of farmers.
{"title":"Rule of Land Potential for Paddy Use Rough Set Method","authors":"Susi Darmawaningsih","doi":"10.22146/IJCCS.69173","DOIUrl":"https://doi.org/10.22146/IJCCS.69173","url":null,"abstract":"Blitar district has become one of the many cities in Java the land situation is largely a good soil of vuikanik to be used as farmland. Agriculture is one of the priority sectors in Blitar district and is supported by culture, geographical conditions and the number of people whose livelihoods are farmers.Hence, it requires a way of knowing where a region might have a potential paddy commodity. It is hoped that the government of blitar will be able to make the best use of the number of paddy commodities produced in blitar district with the many farmers available. A rough set is able to produce information with a rule pattern (rule) which can determine the potential areas for paddy commodities in Blitar district by using factors of harvested area, production amount, and number of farmers per sub-district. This research is not only done analytically but also help from Rosetta's software to test analytic data analysis use rough set. The result of this study is rule as many as 38 rule that can explain the possibility of stake based on the 3 decision attributes: potential, low potential, and not potential. For those areas there is a good chance paddy commodity potential area based on the rules that have been formed is area have a large crop, a large amount of paddy produced, and a small number of farmers.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46816593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automatic text summarization is a representation of a document that contains the essence or main focus of the document. Text summarization is automatically performed using the extraction method. The extraction method summarizes by copying the text that is considered the most important or most informative from the source text into a summary [1]. Documents can be divided into two types, namely single documents and multi documents. Multi document is input that comes from many documents from one or more sources that have more than one main idea.This study aims to summarize the text using a Genetic Algorithm by paying attention to the extraction of text features on each chromosome. The feature extraction used is sentence position, positive keywords, negative keywords, similarity between sentences, sentences containing entity words, sentences containing numbers, sentence length, connections between sentences, the number of connections between sentences. The number of chromosomes used is half of the number of public complaints. The data used is data on public complaints against the DIY government from February 2018 to July 2020. The data is obtained from the e-lapor DIY website. From the test results, the average value of Precision 1, Recall is 0.71, and f-measure value is 0.79.
{"title":"Text Summarization in Multi Document Using Genetic Algorithm","authors":"Nirwana Hendrastuty, Azhari Sn","doi":"10.22146/IJCCS.66026","DOIUrl":"https://doi.org/10.22146/IJCCS.66026","url":null,"abstract":"Automatic text summarization is a representation of a document that contains the essence or main focus of the document. Text summarization is automatically performed using the extraction method. The extraction method summarizes by copying the text that is considered the most important or most informative from the source text into a summary [1]. Documents can be divided into two types, namely single documents and multi documents. Multi document is input that comes from many documents from one or more sources that have more than one main idea.This study aims to summarize the text using a Genetic Algorithm by paying attention to the extraction of text features on each chromosome. The feature extraction used is sentence position, positive keywords, negative keywords, similarity between sentences, sentences containing entity words, sentences containing numbers, sentence length, connections between sentences, the number of connections between sentences. The number of chromosomes used is half of the number of public complaints. The data used is data on public complaints against the DIY government from February 2018 to July 2020. The data is obtained from the e-lapor DIY website. From the test results, the average value of Precision 1, Recall is 0.71, and f-measure value is 0.79.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44764162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I Ketut Adi Wirayasa, Arko Djajadi, H. Santoso, Eko Indrajit
Classification of ordinal data is part of categorical data. Ordinal data consists of features with values based on order or ranking. The use of machine learning methods in Human Resources Management is intended to support decision-making based on objective data analysis, and not on subjective aspects. The purpose of this study is to analyze the relationship between features, and whether the features used as objective factors can classify, and predict certain talented employees or not. This study uses a public dataset provided by IBM analytics. Analysis of the dataset using statistical tests, and confirmatory factor analysis validity tests, intended to determine the relationship or correlation between features in formulating hypothesis testing before building a model by using a comparison of four algorithms, namely Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Artificial Neural Networks. The test results are expressed in the Confusion Matrix, and report classification of each model. The best evaluation is produced by the SVM algorithm with the same Accuracy, Precision, and Recall values, which are 94.00%, Sensitivity 93.28%, False Positive rate 4.62%, False Negative rate 6.72%, and AUC-ROC curve value 0.97 with an excellent category in performing classification of the employee talent prediction model.
{"title":"Comparison Non-Parametric Machine Learning Algorithms for Prediction of Employee Talent","authors":"I Ketut Adi Wirayasa, Arko Djajadi, H. Santoso, Eko Indrajit","doi":"10.22146/IJCCS.69366","DOIUrl":"https://doi.org/10.22146/IJCCS.69366","url":null,"abstract":"Classification of ordinal data is part of categorical data. Ordinal data consists of features with values based on order or ranking. The use of machine learning methods in Human Resources Management is intended to support decision-making based on objective data analysis, and not on subjective aspects. The purpose of this study is to analyze the relationship between features, and whether the features used as objective factors can classify, and predict certain talented employees or not. This study uses a public dataset provided by IBM analytics. Analysis of the dataset using statistical tests, and confirmatory factor analysis validity tests, intended to determine the relationship or correlation between features in formulating hypothesis testing before building a model by using a comparison of four algorithms, namely Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Artificial Neural Networks. The test results are expressed in the Confusion Matrix, and report classification of each model. The best evaluation is produced by the SVM algorithm with the same Accuracy, Precision, and Recall values, which are 94.00%, Sensitivity 93.28%, False Positive rate 4.62%, False Negative rate 6.72%, and AUC-ROC curve value 0.97 with an excellent category in performing classification of the employee talent prediction model.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46254691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Muludi, Mohammad Akbar, D. A. Shofiana, A. Syarif
Sentiment analysis is part of computational research that extracts textual data to obtain positive, or negative values related to a topic. In recent research, data are commonly acquired from social media, including Twitter, where users often provide their personal opinion about a particular subject. Energy independence was once a trending topic discussed in Indonesia, as the opinions are diverse, pros and cons, making it interesting to be analyzed. Deep learning is a branch of machine learning consisting of hidden layers of neural networks by applying non-linear transformations and high-level model abstractions in large databases. The recurrent neural network (RNN) is a deep learning method that processes data repeatedly, primarily suitable for handwriting, multi-word data, or voice recognition. This study compares three algorithms: Simple Neural Network, Bernoulli Naive Bayes, and Long Short-Term Memory (LSTM) in sentiment analysis using the energy independence data from Twitter. Based on the results, the Simple Recurrent Neural Network shows the best performance with an accuracy value of 78% compared to Bernoulli Naive Bayes value of 67% and LSTM with an accuracy value of 75%. Keywords— Sentiment Analysis; Simple RNN; LSTM; Bernoulli Naive Bayes; Energy Independence;
{"title":"Sentiment Analysis Of Energy Independence Tweets Using Simple Recurrent Neural Network","authors":"K. Muludi, Mohammad Akbar, D. A. Shofiana, A. Syarif","doi":"10.22146/IJCCS.66016","DOIUrl":"https://doi.org/10.22146/IJCCS.66016","url":null,"abstract":"Sentiment analysis is part of computational research that extracts textual data to obtain positive, or negative values related to a topic. In recent research, data are commonly acquired from social media, including Twitter, where users often provide their personal opinion about a particular subject. Energy independence was once a trending topic discussed in Indonesia, as the opinions are diverse, pros and cons, making it interesting to be analyzed. Deep learning is a branch of machine learning consisting of hidden layers of neural networks by applying non-linear transformations and high-level model abstractions in large databases. The recurrent neural network (RNN) is a deep learning method that processes data repeatedly, primarily suitable for handwriting, multi-word data, or voice recognition. This study compares three algorithms: Simple Neural Network, Bernoulli Naive Bayes, and Long Short-Term Memory (LSTM) in sentiment analysis using the energy independence data from Twitter. Based on the results, the Simple Recurrent Neural Network shows the best performance with an accuracy value of 78% compared to Bernoulli Naive Bayes value of 67% and LSTM with an accuracy value of 75%. Keywords— Sentiment Analysis; Simple RNN; LSTM; Bernoulli Naive Bayes; Energy Independence;","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44749339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
BPJS Kesehatan, which has been in existence for almost a decade, is still experiencing a deficit in the process of guaranteeing participants. One of the factors that causes this is a discrepancy in the claim process which tends to harm BPJS Kesehatan. For example, by increasing the diagnostic coding so that the claim becomes bigger, making double claims or even recording false claims. These actions are based on government regulations is including fraud. Fraud can be detected by looking at the anomalies that appear in the claim data.This research aims to determine the anomaly of hospital claim to BPJS Kesehatan. The data used is BPJS claim data for 2015-2016. While the algorithm used is a combination of K-Means algorithm and Linear Regression. For optimal clustering results, density canopy algorithm was used to determine the initial centroid.Evaluation using silhouete index resulted in value of 0.82 with number of clusters 5 and RMSE value from simple linear regression modeling of 0.49 for billing costs and 0.97 for length of stay. Based on that, there are 435 anomaly points out of 10,000 data or 4.35%. It is hoped that with the identification of these, more effective follow-up can be carried out.
{"title":"Anomaly Detection in Hospital Claims Using K-Means and Linear Regression","authors":"Hendri Kurniawan Prakosa, N. Rokhman","doi":"10.22146/IJCCS.68160","DOIUrl":"https://doi.org/10.22146/IJCCS.68160","url":null,"abstract":" BPJS Kesehatan, which has been in existence for almost a decade, is still experiencing a deficit in the process of guaranteeing participants. One of the factors that causes this is a discrepancy in the claim process which tends to harm BPJS Kesehatan. For example, by increasing the diagnostic coding so that the claim becomes bigger, making double claims or even recording false claims. These actions are based on government regulations is including fraud. Fraud can be detected by looking at the anomalies that appear in the claim data.This research aims to determine the anomaly of hospital claim to BPJS Kesehatan. The data used is BPJS claim data for 2015-2016. While the algorithm used is a combination of K-Means algorithm and Linear Regression. For optimal clustering results, density canopy algorithm was used to determine the initial centroid.Evaluation using silhouete index resulted in value of 0.82 with number of clusters 5 and RMSE value from simple linear regression modeling of 0.49 for billing costs and 0.97 for length of stay. Based on that, there are 435 anomaly points out of 10,000 data or 4.35%. It is hoped that with the identification of these, more effective follow-up can be carried out.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48531962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sarcasm is one of the problem that affect the result of sentiment analysis. According to Maynard and Greenwood (2014), performance of sentiment analysis can be improved when sarcasm also identified. Some research used Naïve Bayes and Random Forest method on sentiment analysis process. On Salles, dkk (2018) research, in some cases Random Forest outperform the performance by Support Vector Machine that known as a superior method. In this research, we did sentiment analysis on comment section on Instagram account of Indonesian politician. This research compare the accuracy of sentiment analysis with sarcasm detection and analysis sentiment without sarcasm detection, sentiment analysis with Naïve Bayes and Random Forest method then Random Forest for sarcasm detection. This research resulted in accuracy value in sentiment analysis without sarcasm detection with Naïve Bayes 61%, with Random Forest method 72%. Accuracy on sentiment analysis with sarcasm detection using Naïve Bayes – Random Forest method is 60% and using Random Forest – Random Forest method is 71%.
{"title":"Sentiment Analysis With Sarcasm Detection On Politician’s Instagram","authors":"Aisyah Muhaddisi","doi":"10.22146/IJCCS.66375","DOIUrl":"https://doi.org/10.22146/IJCCS.66375","url":null,"abstract":"Sarcasm is one of the problem that affect the result of sentiment analysis. According to Maynard and Greenwood (2014), performance of sentiment analysis can be improved when sarcasm also identified. Some research used Naïve Bayes and Random Forest method on sentiment analysis process. On Salles, dkk (2018) research, in some cases Random Forest outperform the performance by Support Vector Machine that known as a superior method. In this research, we did sentiment analysis on comment section on Instagram account of Indonesian politician. This research compare the accuracy of sentiment analysis with sarcasm detection and analysis sentiment without sarcasm detection, sentiment analysis with Naïve Bayes and Random Forest method then Random Forest for sarcasm detection. This research resulted in accuracy value in sentiment analysis without sarcasm detection with Naïve Bayes 61%, with Random Forest method 72%. Accuracy on sentiment analysis with sarcasm detection using Naïve Bayes – Random Forest method is 60% and using Random Forest – Random Forest method is 71%.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45084460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mechanical keyboards are designed with various shapes, variations, and specifications that are different from other types of keyboards. The mechanical keyboard itself has an aesthetic function that allows users to customize it. There are various specifications on mechanical keyboards, causing various considerations, which can make it difficult for users to choose a mechanical keyboard that fits the desired criteria. Supported by observations in the Indonesia Mechanical Keyboard Group (IMKG), some users are still limited in their knowledge of mechanical keyboard products available in Indonesia, also, currently there is no solution that can handle this problem.Based on these problems, in this research, an DSS is built that can help overcome these problems, by providing recommendations for a mechanical keyboard according to the wishes of the user. DSS is implemented in web form using the AHP method for the weighting process and Profile Matching for the scoring process. The criteria used are determined by conducting a survey regarding the specifications that are the priority considerations in choosing a mechanical keyboard.At the end of the study, the DSS that was successfully built was able to provide mechanical keyboard priority recommendations according to user preferences and get an average evaluation result of 36.17 out of a total maximum value of 40.
{"title":"DSS for Keyboard Mechanical Selection Using AHP and Profile Matching Method","authors":"Amelia Dita Handayani, Retantyo Wardoyo","doi":"10.22146/IJCCS.67813","DOIUrl":"https://doi.org/10.22146/IJCCS.67813","url":null,"abstract":"Mechanical keyboards are designed with various shapes, variations, and specifications that are different from other types of keyboards. The mechanical keyboard itself has an aesthetic function that allows users to customize it. There are various specifications on mechanical keyboards, causing various considerations, which can make it difficult for users to choose a mechanical keyboard that fits the desired criteria. Supported by observations in the Indonesia Mechanical Keyboard Group (IMKG), some users are still limited in their knowledge of mechanical keyboard products available in Indonesia, also, currently there is no solution that can handle this problem.Based on these problems, in this research, an DSS is built that can help overcome these problems, by providing recommendations for a mechanical keyboard according to the wishes of the user. DSS is implemented in web form using the AHP method for the weighting process and Profile Matching for the scoring process. The criteria used are determined by conducting a survey regarding the specifications that are the priority considerations in choosing a mechanical keyboard.At the end of the study, the DSS that was successfully built was able to provide mechanical keyboard priority recommendations according to user preferences and get an average evaluation result of 36.17 out of a total maximum value of 40.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42340497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Sugiartawan, I. M. Yudiana, Paholo Iman Prakoso
The most important thing that can be done by the company, namely the employee selection process, in order to guarantee the right candidate in the right position as well with value-form Organizational Citizenship Behavior. In this study. The methods that can be applied in the career path process in an organization. By implementing a group decision support system, where the opinions of several decision-makers can be accommodated, as well as in problem-solving and communication occurs in a group. This study uses the profile matching method because it can provide an assessment of the potential of each employee candidate by comparing the employee's personal profile with the profile of the position in question, combined with fuzzy logic so that the original value obtained by the alternative remains consistent from the beginning to the ranking process. The results obtained in the form of ranking reports using the Borda method, based on calculations from the fuzzy profile matching method, are expected to help company organizations to facilitate the promotion process.
{"title":"Group Decision Support System Fuzzy Profile Matching Method With Organizational Citizenship Behaviour","authors":"P. Sugiartawan, I. M. Yudiana, Paholo Iman Prakoso","doi":"10.22146/IJCCS.70047","DOIUrl":"https://doi.org/10.22146/IJCCS.70047","url":null,"abstract":"The most important thing that can be done by the company, namely the employee selection process, in order to guarantee the right candidate in the right position as well with value-form Organizational Citizenship Behavior. In this study. The methods that can be applied in the career path process in an organization. By implementing a group decision support system, where the opinions of several decision-makers can be accommodated, as well as in problem-solving and communication occurs in a group. This study uses the profile matching method because it can provide an assessment of the potential of each employee candidate by comparing the employee's personal profile with the profile of the position in question, combined with fuzzy logic so that the original value obtained by the alternative remains consistent from the beginning to the ranking process. The results obtained in the form of ranking reports using the Borda method, based on calculations from the fuzzy profile matching method, are expected to help company organizations to facilitate the promotion process.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47997360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The technical improvements of the present era necessitate that everyone understand information and communication technology. The influence can be useful in a range of industries, especially in the workplace. The office management system is a sort of administrative activity aimed at increasing management effectiveness. As a result, data management at PT. Dwimatama Multikarsa Semarang continues to be done manually, particularly in the production department, with data being input into Microsoft Excel software and stored on hard drives or flash drives. In this case, it is ineffective, especially if the data has been lost or corrupted. The author has come up with the idea of computerizing the administration and archiving system in light of the limitations that have been stated. The author uses a sequential searching approach to do a data search. This method will allow users to find information more quickly and effectively. The system was built using the Laravel framework and the Hypertext Preprocessor (PHP) programming language. The study's conclusion is a web-based data management and storage system that uses MySQL databases. Employees can benefit from this technology by being able to handle and save information more effectively and efficiently.
{"title":"Management System Fertilizer Ship Arrival At UPP Semarang Based Website Using Sequential Searching Algorithm","authors":"S. Susanto, Alda Hani Meidina","doi":"10.22146/IJCCS.68204","DOIUrl":"https://doi.org/10.22146/IJCCS.68204","url":null,"abstract":"The technical improvements of the present era necessitate that everyone understand information and communication technology. The influence can be useful in a range of industries, especially in the workplace. The office management system is a sort of administrative activity aimed at increasing management effectiveness. As a result, data management at PT. Dwimatama Multikarsa Semarang continues to be done manually, particularly in the production department, with data being input into Microsoft Excel software and stored on hard drives or flash drives. In this case, it is ineffective, especially if the data has been lost or corrupted. The author has come up with the idea of computerizing the administration and archiving system in light of the limitations that have been stated. The author uses a sequential searching approach to do a data search. This method will allow users to find information more quickly and effectively. The system was built using the Laravel framework and the Hypertext Preprocessor (PHP) programming language. The study's conclusion is a web-based data management and storage system that uses MySQL databases. Employees can benefit from this technology by being able to handle and save information more effectively and efficiently.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45671234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Epilepsy, cured by some offered treatments such as medication, surgery, and dietary plan, is a neurological brain disorder due to disturbed nerve cell activity characterized by repeated seizures. Electroencephalographic (EEG) signal processing detects and classifies these seizures as one of the abnormality types in the brain within temporal and spectral content. The proposed method in this paper employed a combination of two feature extractions, namely coarse-grained and fractal dimension, a challenge to obtain a highly accurate procedure to evaluate and predict the epileptic EEG signal of normal, interictal, and seizure classes. The result of classification accuracy using variance fractal dimension (VFD) and quadratic support machine vector (SVM) with a number scale of 10 is 99% as the highest one, excellent performance of the predictive model in terms of the error rate. In addition, a higher scale number does not determine a higher accuracy in this study.
{"title":"Combination of Coarse-Grained Procedure and Fractal Dimension for Epileptic EEG Classification","authors":"Dien Rahmawati, Achmad Rizal, D. K. Silalahi","doi":"10.22146/IJCCS.69845","DOIUrl":"https://doi.org/10.22146/IJCCS.69845","url":null,"abstract":" Epilepsy, cured by some offered treatments such as medication, surgery, and dietary plan, is a neurological brain disorder due to disturbed nerve cell activity characterized by repeated seizures. Electroencephalographic (EEG) signal processing detects and classifies these seizures as one of the abnormality types in the brain within temporal and spectral content. The proposed method in this paper employed a combination of two feature extractions, namely coarse-grained and fractal dimension, a challenge to obtain a highly accurate procedure to evaluate and predict the epileptic EEG signal of normal, interictal, and seizure classes. The result of classification accuracy using variance fractal dimension (VFD) and quadratic support machine vector (SVM) with a number scale of 10 is 99% as the highest one, excellent performance of the predictive model in terms of the error rate. In addition, a higher scale number does not determine a higher accuracy in this study.","PeriodicalId":31625,"journal":{"name":"IJCCS Indonesian Journal of Computing and Cybernetics Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44915649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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